Data-driven Bayesian inference for stochastic model identification of nonlinear aeroelastic systems

Abstract

The objective of this work is to propose a data-driven Bayesian inference framework to efficiently identify parameters and select models of nonlinear aeroelastic systems. The framework consists of the use of Bayesian theory together with advanced Kriging surrogate models to effectively represent the limit cycle oscillation response of nonlinear aeroelastic systems. Three types of sampling method, namely Markov Chain Monte-Carlo, Transitional Markov Chain
Monte-Carlo, and the Sequential Monte-Carlo sampler, are implemented into Bayesian model updating. The framework has been demonstrated using a nonlinear wing flutter test rig. It is modelled by a two-degree-of-freedom aeroelastic system and solved by the harmonic balance methods. The experimental data of the flutter wing is obtained using control-based continuation techniques. The proposed methodology provided up to 20% improvement in accuracy compared to conventional deterministic methods and significantly increase computational efficiency in the updating and uncertainty quantification process. Transitional Markov Chain Monte-Carlo was identified as the optimal choice of the sampling method for stochastic model identification. In selecting alternative nonlinear models, multi-modal solutions were identified that provided a closer representation of physical behaviour of the complex aeroelastic system than the single solution.

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ORCID for Jie Yuan: orcid.org/0000-0002-2411-8789

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